Developing And Implementing Planning Heuristics In Prolog

2005

Ontologies are becoming increasingly important in several AI fields (such as knowledge management and integration, cooperative problem solving, knowledge acquisition and knowledge-based systems, e-commerce and the Semantic Web) and, at present, there is also an increasing interest about their use in Planning and Scheduling (P&S) systems.

Theory and Practice of Logic Programming, 2014

Tabling has been used for some time to improve efficiency of Prolog programs by memorizing answered queries. The same idea can be naturally used to memorize visited states during search for planning. In this paper we present a planner developed in the Picat language to solve the Petrobras planning problem. Picat is a novel Prolog-like language that provides pattern matching, deterministic and non-deterministic rules, and tabling as its core modelling and solving features. We demonstrate these capabilities using the Petrobras problem, where the goal is to plan transport of cargo items from ports to platforms using vessels with limited capacity. Monte Carlo Tree Search has been so far the best technique to tackle this problem and we will show that by using tabling we can achieve much better runtime efficiency and better plan quality.

Artificial Intelligence, 2001

In Part I of this series of papers, we have proposed a new logic-based planning language, called Ã. This language facilitates the description of transitions between states of knowledge and it is well suited for planning under incomplete knowledge. Nonetheless, à also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, proving to be very flexible. In the present Part II, we describe the DLV à planning system, which implements à on top of the disjunctive logic programming system DLV. This novel planning system allows for solving hard planning problems, including secure planning under incomplete initial states (often called conformant planning in the literature), which cannot be solved at all by other logic-based planning systems such as traditional satisfiability planners. We present a detailed comparison of the DLV à system to several state-of-the-art conformant planning systems, both at the level of system features and on benchmark problems. Our results indicate that, thanks to the power of knowledge-state problem encoding, the DLV à system is competitive even with special purpose conformant planning systems, and it often supplies a more natural and simple representation of the planning problems.

Artificial Intelligence, 2003

In Part I of this series of papers, we have proposed a new logic-based planning language, called Ã. This language facilitates the description of transitions between states of knowledge and it is well suited for planning under incomplete knowledge. Nonetheless, à also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, proving to be very flexible. In the present Part II, we describe the DLV à planning system, which implements à on top of the disjunctive logic programming system DLV. This novel planning system allows for solving hard planning problems, including secure planning under incomplete initial states (often called conformant planning in the literature), which cannot be solved at all by other logic-based planning systems such as traditional satisfiability planners. We present a detailed comparison of the DLV à system to several state-of-the-art conformant planning systems, both at the level of system features and on benchmark problems. Our results indicate that, thanks to the power of knowledge-state problem encoding, the DLV à system is competitive even with special purpose conformant planning systems, and it often supplies a more natural and simple representation of the planning problems.

ACM Transactions on Computational Logic, 2004

In Part I of this series of papers, we have proposed a new logic-based planning language, called Ã. This language facilitates the description of transitions between states of knowledge and it is well suited for planning under incomplete knowledge. Nonetheless, à also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, proving to be very flexible. In the present Part II, we describe the DLV à planning system, which implements à on top of the disjunctive logic programming system DLV. This novel planning system allows for solving hard planning problems, including secure planning under incomplete initial states (often called conformant planning in the literature), which cannot be solved at all by other logic-based planning systems such as traditional satisfiability planners. We present a detailed comparison of the DLV à system to several state-of-the-art conformant planning systems, both at the level of system features and on benchmark problems. Our results indicate that, thanks to the power of knowledge-state problem encoding, the DLV à system is competitive even with special purpose conformant planning systems, and it often supplies a more natural and simple representation of the planning problems.

Practical Aspects of …, 2001

The goal of this paper is to test if a programming methodology based on the declarative language A-Prolog, algorithms for computing answer sets of programs of A-Prolog, and programming systems implementing these algorithms can be successfully applied to the development of medium size knowledge-intensive applications. We report on a successful design and development of such a system controlling some of the functions of the Space Shuttle.

Latin-American Workshop on Non-Monotonic Reasoning, 2007

This paper presents a system that controls the behavior of a mobile robot. The system is based on situation calculus, the initial state is described and a goal is given, Prolog produces an answer to this goal and we use an interface in Visual Basic to interpret the answer given by it. After of interpreting the given actions in response

We briefly describe the logic programming language PROLOG concentrating on those aspects of the language that make it suitable for implementing expert systems. We show how features of expert systems such as: (1) inference generated requests for data, (2) probabilistic reasoning, (3) explanation of behaviour can be easily programmed in PROLOG. We illustrate each of these features by showing how a fault finder expert could be programmed in PROLOG.

2013

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2009